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Статті в журналах з теми "Video Quality Measure"
Barkowsky, Marcus, Jens Bialkowski, BjÖrn Eskofier, Roland Bitto, and AndrÉ Kaup. "Temporal Trajectory Aware Video Quality Measure." IEEE Journal of Selected Topics in Signal Processing 3, no. 2 (April 2009): 266–79. http://dx.doi.org/10.1109/jstsp.2009.2015375.
Повний текст джерелаHekstra, A. P., J. G. Beerends, D. Ledermann, F. E. de Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss. "PVQM – A perceptual video quality measure." Signal Processing: Image Communication 17, no. 10 (November 2002): 781–98. http://dx.doi.org/10.1016/s0923-5965(02)00056-5.
Повний текст джерелаArndt, Sebastian, Jan-Niklas Antons, Robert Schleicher, Sebastian Moller, and Gabriel Curio. "Using Electroencephalography to Measure Perceived Video Quality." IEEE Journal of Selected Topics in Signal Processing 8, no. 3 (June 2014): 366–76. http://dx.doi.org/10.1109/jstsp.2014.2313026.
Повний текст джерелаDumic, Emil, and Anamaria Bjelopera. "No-Reference Objective Video Quality Measure for Frame Freezing Degradation." Sensors 19, no. 21 (October 26, 2019): 4655. http://dx.doi.org/10.3390/s19214655.
Повний текст джерелаLi, Gang, Ainiwaer Aizimaiti, and Yan Liu. "Quaternion Model of Fast Video Quality Assessment Based on Structural Similarity Normalization." Applied Mechanics and Materials 380-384 (August 2013): 3982–85. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3982.
Повний текст джерелаLeszczuk, Mikołaj, Lucjan Janowski, Jakub Nawała, and Atanas Boev. "Objective Video Quality Assessment Method for Face Recognition Tasks." Electronics 11, no. 8 (April 7, 2022): 1167. http://dx.doi.org/10.3390/electronics11081167.
Повний текст джерелаAlsrehin, Nawaf O., and Ahmad F. Klaib. "VMQ: an algorithm for measuring the Video Motion Quality." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 231–38. http://dx.doi.org/10.11591/eei.v8i1.1418.
Повний текст джерелаMoore, Peter Thomas, Neil O’Hare, Kevin P. Walsh, Neil Ward, and Niamh Conlon. "Objective video quality measure for application to tele-echocardiography." Medical & Biological Engineering & Computing 46, no. 8 (July 10, 2008): 807–13. http://dx.doi.org/10.1007/s11517-008-0364-5.
Повний текст джерелаWu, Xuanyi, Irene Cheng, Zhenkun Zhou, and Anup Basu. "RAVA: Region-Based Average Video Quality Assessment." Sensors 21, no. 16 (August 15, 2021): 5489. http://dx.doi.org/10.3390/s21165489.
Повний текст джерелаHasan, Md Mehedi, Md Ariful Islam, Sejuti Rahman, Michael R. Frater, and John F. Arnold. "No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System." Applied Sciences 12, no. 19 (October 7, 2022): 10090. http://dx.doi.org/10.3390/app121910090.
Повний текст джерелаДисертації з теми "Video Quality Measure"
Pettersson, Johan, and Robin Veteläinen. "A comparison of solutions to measure Quality of Service for video streams." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188514.
Повний текст джерелаDet finns fler och fler personer som tittar på video strömmar på Internet, detta har lett till att nya företag har startats som konkurerar om tittare. För att förbättra kundupplevelsen kan man mäta hur tjänsten presterar. Målet med examensarbetet var att rekommendera hur man kan mäta tjänstekvalite för en realtidsvideoströmningstjänst. Tre olika lösningsförslag presenterades; att köpa informationen från en content delivery network, att bygga vidare på tillgängliga analytisk mjukvara eller att bygga ett eget paketsniffarprogram. Det bestämdes att bygga vidare på tillgänglig analytisk mjukvara. Fyra olika mjukvara jämfördes: Google Analytics, Mixpanel, Ooyala IQ och Piwik. Jämförelsen gjordes med hjälp av analytical hierarchy process, de olika alternativen jämfördes med avseende på: hur moget API:et var, flexibilitet, visualiseringen av data och support. Rekommendationen är att använda sig av Ooyala IQ som utmärker sig med avseende på flexibilitet, det var enkelt att använda deras API i sin egen lösning, det fanns ingen gräns på hur många händelser man kunde lagra per månad, och slutligen så fanns det dedikerad supportpersonal att nå via telefon eller email.
Intawong, Kannikar. "Analyse automatique de la circulation automobile par vidéosurveillance routière." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2081.
Повний текст джерелаThis thesis is written in the context of video traffic analysis. In several big cities, hundreds of cameras produce very large amounts of data, impossible to handle without automatic processing. Our main goal is to help human operators by automatically analyzing video data. To help traffic controllers make decisions, it is important to know the traffic status in real time (number of vehicles and vehicle speed on each path), but also to dispose of traffic statistics along the day, week, season or year. The cameras have been deployed for a long time for traffic and other monitoring purposes, because they provide a rich source of information for human comprehension. Video analysis can automatically extract relevant information. Computer vision and video analysis are becoming more and more important for Intelligent Transport Systems (ITSs). One of the issues addressed in this thesis is related to automatic vehicle counting. In order to be useful, a video surveillance system must be fully automatic and capable of providing, in real time, information concerning the behavior of the objects in the scene. We can get this information by detection and tracking of moving objects in videos, a widely studied field. However, most automated video analysis systems do not easily manage particular situations.Today, there are many challenges to be solved, such as occlusions between different objects, long stops of an object in the scene, luminosity changes, etc., leading to incomplete trajectories of moving objects detected in the scene. We have concentrated our work on the automatic extraction of global statistics in the scenes. Our workflow consists of the following steps: first, we evaluated different methods of video segmentation and detection of moving objects. We have chosen a segmentation method based on a parametric version of the Mixture of Gaussians, applied to a hierarchy of blocks, which is currently considered one of the best methods for the detection of moving objects. We proposed a new methodology to choose the optimal parameter values of an algorithm to improve object segmentation by using morphological operations. We were interested in the different criteria for evaluating the segmentation quality, resulting from a compromise between a good detection of moving objects, and a low number of false detections, for example caused by illumination changes, reflections or acquisition noises. Secondly, we performed an objects classification, based on Fourier descriptors, and we use these descriptors to eliminate pedestrian or other objects and retain only vehicles. Third, we use a motion model and a descriptor based on the dominant colors to track the extracted objects. Because of the difficulties mentioned above, we obtain incomplete trajectories, which, exploited as they are, give incorrect counting information. We therefore proposed to aggregate the partial data of the incomplete trajectories and to construct a global information on the vehicles circulation in the scene. Our approach allows to detect input and output points in image sequences. We tested our algorithms on private data from the traffic control center in Chiang Mai City, Thailand, as well as on MIT public video data. On this last dataset, we compared the performance of our algorithms with previously published articles using the same data. In several situations, we illustrate the improvements made by our method in terms of location of input / output zones, and in terms of vehicle counting
Saidi, Inès. "Analyse et modélisation de la qualité perçue des applications de visiophonie." Thesis, Rennes, INSA, 2018. http://www.theses.fr/2018ISAR0013/document.
Повний текст джерелаIn a highly competitive environment, one of the key challenges for operators and providers of video telephony services is to ensure the highest quality of experience (QoE). There is a strong need for a measure that reflects users satisfaction and perception of these services. The audio-visual quality of a video call must be controlled to meet two main needs. The first concerns the planning of new technologies under development. The second is focused on the control of existing communications by assessing the quality of the services offered and evaluating them. Two approaches are used to evaluate audio-visual quality: subjective tests by collecting scores given by participants on quality scales, after viewing and listening to audiovisual sequences and objective metrics based on automatic audio/ video or audiovisual quality evaluation algorithms. Concerning telephony services, decades of research, standardization work and network exploitation, have allowed operators to master the automatic monitoring tools and to determine the representative metrics of voice quality. However, the metrics for measuring the audiovisual quality of a conversational services are not yet mature and not exploited by telecommunication operators. The present work focuses on finding representative metrics of the perception of the video telephony anc videoconferencing services quality. These objective metrics are calculated from the audio and video signals. Subjective tests are conducted to collect the judgment of service users on the perceived quality according to different levels of degradation. We studied the impact of network conditions (packet loss, jitter and desynchronization) on the QoE of a video call. The general principle is then to establish a correlation between the selected objective metrics and the perceived quality as expressed by the users. The results showed that new metric of overall audiovisual quality that take into account the temporal aspect of video are more powerful than image quality based metrics. On the other hand, the use of a machine learning approach represents a solution to generat a global quality prediction model from the degradation metrics (blur, pixelization, image freezing, ... )
Černoch, Adam. "Vybrané způsoby zlepšení orientace řidiče v dopravním prostoru." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-233053.
Повний текст джерелаGrigoryKuritsyn and 柯高瑞. "Service Quality in Video Surveillance-as-a-Service: Developing Measures and Analyzing Its Function in Continuance Usage Intention." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/91460510252474392991.
Повний текст джерела國立成功大學
國際經營管理研究所碩士在職專班
102
Video surveillance is one of the most important components of the complete security solution. With the development of the Internet and technologies this important piece of safety is available for average customers not only locally, but also remotely with the help of cloud computing technologies. In the scientific works there are no studies that cover this topic so far. This research aims to find the major factors that will affect customers’ perception of the service quality, and reasons that will drive customers to keep using the service. As it was found, trust can play an important role in identifying the continuance intention to use services; and the seven major factors of VSaaS quality (rapport, reliability, responsiveness, flexibility, features, security, and privacy) would be the major players in defining quality, and hence perceived usefulness and continuous intention to use the services.
Книги з теми "Video Quality Measure"
L, Warholak Terri, and Nau David P, eds. Quality and safety in pharmacy practice. New York: McGraw-Hill, 2010.
Знайти повний текст джерелаCohen, Marcy. Playing with our health: Hazards in the automated office. Vancouver: Women's Skill Development Society, 1986.
Знайти повний текст джерелаMontana. Legislature. Office of the Legislative Auditor. Performance audit report: Regulation and monitoring of video gambling machines, Department of Justice, Gambling Control Division. Helena, Mont: The Office, 1994.
Знайти повний текст джерелаMontana. Legislature. Office of the Legislative Auditor. Performance audit report: Air quality program, Department of Health and Environmental Sciences. Helena, Mont: The Office, 1994.
Знайти повний текст джерелаMontana. Legislature. Office of the Legislative Auditor. Performance audit report: Enforcement of the water quality and the public water supply acts. Helena, Mont: The Office, 1994.
Знайти повний текст джерелаAuditor, Montana Legislature Office of the Legislative. Performance audit report: Controls over use of state telephones. Helena, Mont: The Office, 1986.
Знайти повний текст джерелаAuditor, Montana Legislature Office of the Legislative. Performance audit report: Automated system development and maintenance, Department of Administration, Department of Social and Rehabilitation Services, Department of Labor and Industry. Helena, Mont: The Office, 1993.
Знайти повний текст джерелаMontana. Legislature. Office of the Legislative Auditor. Performance audit report: Space utilization and records management, Helena-located state agencies. Helena, Mont. (Room 135, State Capitol 59620): The Office, 1989.
Знайти повний текст джерелаMontana. Legislature. Office of the Legislative Auditor. Performance audit report: Treasury Bureau, Department of Administration. Helena (Rm. 135, State Capitol, Helena 59620): Office of the Legislative Auditor, 1989.
Знайти повний текст джерелаMontana. Legislature. Office of the Legislative Auditor. Performance audit report: Foster care facility licensing and other related issues, Department of Family Services. Helena, Mont: The Office, 1993.
Знайти повний текст джерелаЧастини книг з теми "Video Quality Measure"
Budescu, Bogdan, Alexandru Căliman, and Mihai Ivanovici. "The Correlation Dimension: A Video Quality Measure." In Mobile Multimedia Communications, 55–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30419-4_5.
Повний текст джерелаEl Khattabi, Hasnaa, Ahmed Tamtaoui, and Driss Aboutajdine. "Measure a Subjective Video Quality Via a Neural Network." In Communications in Computer and Information Science, 121–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21984-9_11.
Повний текст джерелаNoor, Norliza Mohd, Omar Mohd Rijal, Joel Chia Ming Than, Rosminah M. Kassim, and Ashari Yunus. "Regression as a Tool to Measure Segmentation Quality and Preliminary Indicator of Diseased Lungs." In Image and Video Technology, 502–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29451-3_40.
Повний текст джерелаKramer, Maria, Julia Stürmer, Christian Förtsch, Tina Seidel, Stefan Ufer, Martin R. Fischer, and Birgit J. Neuhaus. "Diagnosing the Instructional Quality of Biology Lessons Based on Staged Videos: Developing DiKoBi, A Video-Based Simulation." In Learning to Diagnose with Simulations, 63–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89147-3_6.
Повний текст джерелаGhosh, Monalisa, and Chetna Singhal. "Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks." In Advances in Wireless Technologies and Telecommunication, 235–54. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7458-3.ch010.
Повний текст джерела"Secret Communication Techniques." In Advanced Digital Image Steganography Using LSB, PVD, and EMD, 1–19. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7516-0.ch001.
Повний текст джерелаCavallaro, Andrea, and Stefan Winkler. "Perceptual Semantics." In Multimedia Technologies, 1441–55. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-953-3.ch105.
Повний текст джерелаCavallaro, Andrea, and Stefan Winkler. "Perceptual Semantics." In Digital Multimedia Perception and Design, 1–20. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-860-4.ch001.
Повний текст джерелаSankisa, Arun, Katerina Pandremmenou, Peshala V. Pahalawatta, Lisimachos P. Kondi, and Aggelos K. Katsaggelos. "SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels." In Biometrics, 690–709. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch028.
Повний текст джерелаDuarte, Abraham, Angel Sanchez, Felipe Fernandez, and Antonio S. Montemayor. "A Graph-Based Image Segmentation Alorithm Using Heirarchical Social Metaheuristic." In Advances in Image and Video Segmentation, 72–92. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-753-9.ch004.
Повний текст джерелаТези доповідей конференцій з теми "Video Quality Measure"
Pyko, Sergey, Boris Filippov, Tamar Shoham, Dror Gill, Nikolay Terterov, Alexander Ivanov, and Vadim Demidov. "Porting BQM perceptual video quality measure to hardware." In MHV '22: Mile-High Video Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3510450.3517278.
Повний текст джерелаLi, Songnan, Lin Ma, Fan Zhang, and King Ngi Ngan. "Temporal inconsistency measure for video quality assessment." In 2010 Picture Coding Symposium (PCS). IEEE, 2010. http://dx.doi.org/10.1109/pcs.2010.5702572.
Повний текст джерелаKim, Wonjun, and Changick Kim. "Video quality measure for mobile IPTV service." In Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2008. http://dx.doi.org/10.1117/12.799448.
Повний текст джерелаBai, Chen, and Amy R. Reibman. "Video Quality Temporal Pooling using a Visibility Measure." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00082.
Повний текст джерелаOkamoto, Jun, Keishiro Watanabe, Aoi Honda, Masato Uchida, and Seiichiro Hangai. "HDTV objective video quality assessment method applying fuzzy measure." In 2009 International Workshop on Quality of Multimedia Experience (QoMEx 2009). IEEE, 2009. http://dx.doi.org/10.1109/qomex.2009.5246958.
Повний текст джерелаShadiya, P., and K. P. Balachandran. "A perceptual distortion measure for video quality assessment using wavelet and statistical measures." In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2015. http://dx.doi.org/10.1109/spices.2015.7091376.
Повний текст джерелаKawayoke, Yoshikazu, and Yuukou Horita. "NR objective continuous video quality assessment model based on frame quality measure." In 2008 15th IEEE International Conference on Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icip.2008.4711772.
Повний текст джерелаYao, Susu, Weisi Lin, Eeping Ong, and Zhongkang Lu. "A Wavelet-Based Visible Distortion Measure for Video Quality Evaluation." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.313134.
Повний текст джерелаJeong, Dong-ju, Hyoung Jin Yoo, and Nam Ik Cho. "Consumer video summarization based on image quality and representativeness measure." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418260.
Повний текст джерелаBen Amor, Mohamed, Mohamed Chaker Larabi, Fahmi Kammoun, and Nouri Masmoudi. "A block artifact distortion measure for no reference video quality evaluation." In 2014 First International Image Processing, Applications and Systems Conference (IPAS). IEEE, 2014. http://dx.doi.org/10.1109/ipas.2014.7043326.
Повний текст джерелаЗвіти організацій з теми "Video Quality Measure"
Lee, Yooyoung, P. Jonathon Phillips, James J. Filliben, J. Ross Beveridge, and Hao Zhang. Identifying face quality and factor measures for video. National Institute of Standards and Technology, May 2014. http://dx.doi.org/10.6028/nist.ir.8004.
Повний текст джерела